Thermokinetic study of macadamia carpel pyrolysis using thermogravimetric analysis
Bibliographic record
Abstract
Abstract The macadamia productive process generates a large amount of waste, such as walnut carpel, that is improperly disposed of in most instances. This residue is a promising feedstock for biofuel production and it can be converted through thermochemical routes. Liquid pyrolysis products obtained contain a bio‐oil with great potential for the production of fuels and fine chemicals. The goal of this study was to characterize the macadamia carpel for the first time by ultimate, proximate, FTIR, and TG analysis, as well as HHV determination, which showed this is a potential candidate for thermochemical conversion processes. Thermodynamic and kinetic pyrolysis behaviours were evaluated using Ozawa, Kissinger, Starink, and KAS isoconversional models (140.12, 137.69, 137.41, and 146.63 kJ mol −1 , respectively), which attested to the product favourability and the operational viability. Additionally, the master plot method was applied to complete the kinetic description, which identified diffusion and nucleation mechanisms during the carpel degradation. After 50% conversion, the third‐order reaction mechanism occurred due to a predominant lignin decomposition. The results showed that the macadamia carpel properties are similar to traditional feedstock, that it may be decomposed into bio‐oil, and that it can be used as an alternative energy source or bio‐chemicals precursor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".